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In a hypothetical, infinite sequence of independent repetitions of the whole experiment 35LogicofInferential Separation, Ancillarity and Sufficiency the estimate u of p would follow the distribution with probability function 1 . 1 u- p cp(u ~ p) + —-y (1) lOM 10 where cp is the density of N{0,1). However, the precision in the estimation of p provided by the one experiment actually performed is described not by (1) but by either (p(u — p) or (p[(u — ¿¿)/10]/10, depending on which of the two instruments was in fact used.

Suppose the object is to test the hypothesis that the odds ratio (2) Pl I P2 1 - p j \ - p2 has a particular value, {¡/0 say, the main possibility being, of course, corresponds to px = p2. In relation hereto Fisher remarks: = 1 which 37 Logic of Inferential Separation, Ancillarity and Sufficiency Let us blot out the contents of the table, leaving only the marginal frequencies. If it be admitted that these marginal frequencies by themselves supply no information on the point at issue,... we may...

3. 6 contains some results on the relations between conditional and unconditional plausibility functions. 1 O N INFERENTIAL SEPARATION. ANCILLARITY A N D SUFFICIENCY Let u and v be statistics, let ^ be a (sub)parameter with variation domain ¥ and suppose that the conditional distribution of u given v depends on co through \j/ only, and is in fact parametrized by \j/. Making inference on [¡/ from u and from the conditional model for u given the observed value of v is an act of separate inference.